eudata402

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the burden. It does state the data source (mempool.space) and the nature of the tool (network data), implying a read-only lookup. However, it does not mention rate limits, data freshness, or potential external API dependencies, leaving some behavioral uncertainty.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no unnecessary words. 'Bitcoin network data' leads, followed by the three use cases. Every word contributes to the agent's understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple but has a conditional dependency (kind=tx/address requires id) that is not disclosed. There is no output schema, and the description does not mention what the response looks like. An agent may not know what value to pass for 'id' or that it is needed at all, making the description incomplete for correct use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does map the 'kind' enum values to meaningful operations (fees, tx, address), but it fails to explain the 'id' parameter, which is necessary for tx and address lookups. The schema lists zero required parameters, and the description does not clarify that 'id' is conditionally required, risking incorrect invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides Bitcoin network data from mempool.space, with three specific operations: recommended fee tiers, transaction lookup, and address lookup. This distinguishes it from sibling tools like crypto_price and fx_rates, which cover other financial data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (when Bitcoin network data is needed) but does not explicitly contrast it with alternatives or state exclusions. There is no mention of 'use crypto_price for prices' or similar guidance, so usage is inferred rather than clearly directed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It names the data source and signals a read-only lookup, but it omits important behavior such as price denomination, response format, failure modes, or unsupported-chain handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise, well-front-loaded sentences with no filler. The first sentence identifies the operation and inputs, while the second explains the differentiating value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter lookup, the description covers the essential context: what the tool returns conceptually, the source, and when it applies. It would be fully complete if it stated the output format or price currency, especially since there is no output schema or annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already fully documents both parameters, including chain examples and the meaning of address. The description adds no parameter-level detail beyond what the schema provides, so a baseline of 3 is appropriate for 100% schema description coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's core purpose: returning the current crypto token price for a given chain + contract address, with DefiLlama as the source. It also differentiates itself from broader price-by-symbol APIs, making its niche clear even among finance-related sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The sentence 'Works for tokens that price-by-symbol APIs miss' gives a strong signal for when this tool is useful. It implies the alternative is a symbol-based price lookup, though it does not explicitly name a sibling or state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does communicate that the data comes from the official USGS feed and that results are recent and filterable, which is useful behavioral context. However, it does not disclose return format, default time range, whether an external API is called, or any rate-limit/staleness details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero filler. It front-loads the data source, adds the filtering capability, and closes with relevant use cases. There is no repetition of schema fields or annotation information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description needs to do more work to be actionable. It states the source and filters, but does not explain the output shape, default behavior, or return format. An agent could invoke it, but might not know what to expect back or how to interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description only partially compensates by saying results are filterable by magnitude and time. It does not clarify the meaning of 'limit' or give format details for starttime/endtime, both of which are important for correct invocation. The description adds only coarse parameter context, not per-parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states what the tool provides: recent earthquake data from the official USGS feed, filterable by magnitude and time. It is not merely a tautology, and it is clearly distinct from the sibling tools, none of which cover earthquakes. It lacks an explicit command verb like 'fetches' or 'lists', which keeps it from a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives an explicit context for use (catastrophe, insurance, and logistics risk), which a helpful but basic guidance. It does not mention alternatives or explicit exclusions, but there are no sibling earthquake tools, so confusion is unlikely. This is clear context without full when-to/not-to guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral burden, and it does communicate via 'resolve... via Wikidata' that this is a non-mutating lookup. However, it never explicitly discloses read-only semantics, error or ambiguity behavior, or any source caveats, and 'public record' is a slightly misleading label for a Wikidata-derived profile. Adequate for a simple resolver but no proactive disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single 27-word sentence with no filler: the action verb leads, the object and output fields follow, and the data source closes it out. Every element earns its place, and the structure mirrors how an agent would parse the pipeline: input → resolution → output.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with no output schema, the description fully covers inputs, the resolution step, and the expected return structure. The only gaps are minor for the complexity level: no statement on unresolved-entity behavior or ambiguity handling, and 'inception' could confuse (does it mean founding date?). Otherwise complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with 'query' described as 'Company or entity name', so the schema does the heavy lifting (baseline 3). The description earns a bump by enumerating the cross-linked identifiers returned, giving the agent meaningful signal about what constitutes an effective query and what outputs to expect.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description pairs a specific verb ('Resolve') with a defined resource ('company or entity') and a concrete list of output identifiers (LEI, ISIN, ticker, website, country, inception), plus the source ('via Wikidata'). The 'via Wikidata' mention subtly differentiates it from the LEI and KYB siblings, but this is implicit — the somewhat ambiguous phrase 'public record' and the unexplained 'inception' hold it back from a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is only implied — the agent must infer from 'via Wikidata' when to choose this over lei_lookup, kyb_verify_business, or sanctions_screen, which could all accept a company name. There is no explicit when-to-use or when-not-to-use guidance despite a crowded field of overlapping identifier tools. Minimal but acceptable for a simple lookup.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the return fields and the ordering (newest first), which is useful. However, it does not mention any limitations, error behavior, or whether the operation is read-only (though implied). Missing details like rate limits, result structure, or handling of empty results reduce transparency, but the basic behavior is described.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the purpose and return information. It is concise with no filler or repetition. Every part adds value, making it highly efficient for an agent to quickly understand what the tool does.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with two parameters, no output schema, and no annotations, the description is moderately complete. It explains the return fields and ordering, which helps the agent know what to expect. However, it omits the meaning of the 'limit' parameter and any note on how results are structured (e.g., array of objects). Given the low schema coverage and absence of annotations, the description should have covered these details to be fully contextual.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 50% — only 'query' has a description, while 'limit' does not. The tool description adds no additional meaning for 'limit' (e.g., that it controls result count or pagination). Since the description fails to compensate for the missing schema documentation, param semantics are weak. The description also does not explain how 'query' interacts with the search beyond 'full-text search', which is already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches EU public procurement notices from the official TED source, lists the returned fields (tender id, title, publication date, notice URL), and notes the sorting order (newest first). This specifies the verb, resource, and scope well. It does not explicitly differentiate from sibling tools, but none of the siblings are procurement-related, so differentiation is less critical.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides enough context for when to use the tool (searching EU public procurement notices) without explicit alternatives. It does not state exclusions, but since no sibling tool offers similar functionality, this is acceptable. The description lacks explicit when-not-to-use guidance, but the purpose is clear and self-contained.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It does disclose the external service, the return payload, and the supported member states. However, it does not mention behavior for invalid VAT numbers, VIES unavailability, latency, rate limits, or whether the tool returns a structured result versus throwing an error.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise, information-dense sentences. The primary action and resource are front-loaded, followed by the return value and scope. There is no fluff, and every sentence contributes useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool with full schema coverage, the description is mostly complete: it states purpose, external source, return values, and geographic scope. The main gap is the lack of a precise return shape or error behavior, made slightly more relevant by the absence of an output schema, but this is not a severe omission for this tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and both parameters already have clear explanations: 'VAT number without country prefix' and 'EU member state code, e.g. LT, DE, FR'. The description adds no further parameter-level meaning, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Validate'), a precise resource ('EU VAT number'), and the authoritative source ('official European Commission VIES service'). It also names the expected output, the registered company name and address, and clarifies scope ('All 27 member states'). This clearly distinguishes it from siblings like iban_validate or kyb_verify_business.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The context is implied: use this when you need EU VAT validation against VIES. However, there is no explicit guidance about when not to use it or which sibling tool might be a better alternative for related but different checks, such as IBAN validation or broader business verification.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'optional conversion' but does not explain how conversion works, what the response contains, whether the operation is read-only, or any rate/limit considerations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence with no filler, and the core source ('Official ECB') is front-loaded. It is appropriately sized, though it sacrifices useful detail for brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three parameters, no annotations, and no output schema, the description is under-specified. It leaves conversion semantics, return format, and differentiation from us_treasury_fx unaddressed, making reliable invocation harder for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes 'from' and 'to', covering 67% of parameters. The description's 'optional conversion' hints at the role of the undocumented 'amount' parameter, but does not clarify units, conversion direction, or output behavior, so compensation is only partial.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the resource as official ECB foreign-exchange reference rates and mentions optional conversion. It distinguishes the tool from siblings like crypto_price and us_treasury_fx by source and asset class, though it lacks an explicit verb like 'get' or 'fetch'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as us_treasury_fx or crypto_price. The description does not state any selection criteria, data freshness considerations, or scenarios where conversion is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It states the operation (geolocation) and output fields but does not mention any potential limitations, rate limits, authentication requirements, or side effects. For a lookup tool this is relatively benign, but the description is minimal and leaves the agent to assume read-only behaviour without confirmation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence. It front-loads the core function ('IP geolocation with ASN, ISP and organisation') and then attaches the use cases. There is no wasted wording or redundancy, making it highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema present, the description partially compensates by listing the expected data fields (ASN, ISP, organisation). However, it does not describe the response format, error behaviour, or any constraints beyond the IP parameter. For a simple lookup, it is adequate but leaves some ambiguity about what the result structure will look like.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter 'ip' is fully described in the schema as 'IPv4 or IPv6 address', giving 100% schema coverage. The description does not add any additional meaning beyond the schema, so it meets the baseline for high coverage. It does not conflict with or enrich the schema in any way.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs IP geolocation and specifies the additional data returned (ASN, ISP, organisation). It also identifies two concrete use cases (fraud checks and lead enrichment). This unambiguously distinguishes it from sibling tools like entity_resolve or sanctions_screen, which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions use cases ('for fraud checks and lead enrichment') which gives contextual guidance on when it is appropriate. However, it does not explicitly state when not to use it or name any alternative tools. Since there are many sibling lookups (e.g., entity_resolve, sanctions_screen), the guidance is implicit rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full behavioral disclosure. It explicitly states the validation mechanism, country coverage, and that it makes no external call. It does not describe the return shape, but the read-only, local nature is disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tightly written sentences: the first delivers the core function and algorithm, the second adds a useful performance/architecture trait. No filler, front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter, no-output-schema, no-annotation validation tool, the description covers function, mechanism, coverage, and latency. The only notable gap is whether the return is a plain boolean or a more detailed object, but that omission is minor given the simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema coverage is 100% and the 'iban' parameter is already described as 'IBAN, spaces allowed'. The description adds no new parameter-level meaning beyond the overall validation scope, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific, distinct action: 'Validate an IBAN' with explicit technical scope (ISO 13616 structure + mod-97 checksum) and coverage ('70+ countries'). Clearly distinguishable from siblings like eu_vat_validate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool's use case—when an IBAN needs offline validation—but does not explicitly compare it to alternatives or state when not to use it. The 'Instant, no external call' trait gives context, but no direct exclusions or guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden of revealing behavior. It discloses the core output shape: a single pass/review/fail verdict with confidence and flags, which is genuinely useful. It does not address side effects, rate limits, or partial-parameter behavior, but for a screening tool this is adequate disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact sentences that front-load the high-signal information: what the tool is, what it fuses, and when to call it. Every sentence earns its place and there is no redundant wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The definition includes the overall output format and typical business triggers, which is good for a composite-feature tool. However, with five optional parameters and no output schema, it does not explain minimum required inputs or how partial data affects the verdict, leaving an agent uncertain about how to construct valid calls.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and each parameter already has a short descriptor such as '20-char LEI code' or 'VAT number without country prefix'. The description maps the checks to complementary vocabulary (VIES, GLEIF, checksum) but does not explain required combinations or how many parameters must be supplied for a meaningful verdict.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines a composite KYB/counterparty due-diligence tool and enumerates the five checks it runs. It distinguishes itself from sibling single-purpose validators such as eu_vat_validate, lei_lookup, iban_validate, and sanctions_screen by explicitly saying 'ONE call' and describing a fused verdict.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives concrete, high-value call signals: before onboarding a customer, paying a supplier, or transacting with an EU business. It does not explicitly tell the agent when to use a sibling single-source tool instead, so it lacks an explicit exclusion or alternative rule.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral disclosure burden. It states the tool queries an official registry, implying a read-only operation, but it does not explicitly disclose that no modifications occur, nor does it mention any external API dependencies, rate limits, or response characteristics beyond the listed fields. This is adequate for a simple lookup but leaves some behavior implicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, focused sentence that fronts the core purpose and lists key return fields without extraneous detail. It is efficient and well-structured for an agent to parse quickly, though it could be broken into a bulleted list for even clearer scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description provides a solid picture: the input format, the source (official GLEIF), and the main output fields. It does not cover error cases (e.g., invalid LEI format or no match found), but these are not critical for a basic lookup and the description is adequate for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of the single parameter, but the description adds meaningful detail by specifying that the LEI must be a 20-character code, which the schema description ('LEI code or company legal name') does not. This extra precision helps agents validate input format and adds value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('look up'), the specific resource (Legal Entity Identifier in the official GLEIF registry), and the input scope (20-char LEI code or company legal name). It also lists the returned fields, distinguishing it from sibling tools like entity_resolve or kyb_verify_business which target broader business verification.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided about when to choose this tool over alternatives. The description does not mention any alternative tools or exclusions. An agent must infer from the name and context that this is the dedicated LEI lookup, but no explicit when-to-use/when-not-to-use information is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the return type (hit/risk with clear|review|high and matching entries), which is useful behavioral context. However, it does not mention side effects, authentication needs, rate limits, or potential false-positive behavior. For a screening tool, more transparency about how matches are determined would be valuable, but the basic output is covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no redundancy. It front-loads the core function and lists, then states the compliance use case. Every word serves a purpose, and it is efficiently structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with no output schema, the description covers the essential information: what it screens, the specific lists, the return format, and the use case. It could mention potential limitations or whether the check is real-time, but given the tool's simplicity, this is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'name' is fully described in the schema ('Person or company name to screen'), and the description does not add additional semantics beyond that. Since schema coverage is 100%, this meets the baseline but does not exceed it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool screens a person or company against official sanctions lists and names the specific lists (OFAC SDN, OFAC Consolidated, UK OFSI). The verb 'screen' and resource are specific, distinguishing it from generic lookup tools. It does not explicitly contrast with sibling tools like entity_resolve or kyb_verify_business, but the purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear use case: 'Use for AML/KYC/KYB compliance before onboarding or paying a counterparty.' This gives the agent contextual guidance on when to invoke the tool. It does not mention exclusions or alternatives, but the context is sufficient for a compliance-related screen.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions the source and use case but does not disclose behavior like data freshness, response format, or any access requirements. For a tool that will be called automatically, the agent is left to guess the output nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence, front-loaded with the resource identity and source, and a useful comparison. No redundancy. It loses a point for not leading with an action verb, but it is otherwise tight.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description omits how the rates are returned (e.g., daily vs historical), any date or qualification parameters, and what the response looks like. The intended federal-reporting use case is mentioned, but operational details are missing, leaving the agent under-informed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema provides descriptions for both required parameters (country, currency) with examples. The description adds no further meaning about the parameters. With 100% schema coverage, a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the resource (US Treasury exchange rates) and source (fiscaldata.treasury.gov), and contrasts it with the ECB counterpart. However, it is phrased as a noun phrase rather than an explicit verb+resource formulation like 'returns exchange rates', making the exact action slightly implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides context ('used for US federal reporting') and a comparison ('US-side counterpart to ECB rates'), which hints at when to prefer this tool, but there is no explicit statement of when to use vs. not use, nor any named alternative tool in the sibling list. The guidance is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the behavioral disclosure burden. It discloses the data source (Open-Meteo) and implies a read-only weather lookup that returns current conditions and forecast data. It does not mention rate limits, output format, or data update behavior, but for a simple read-only weather tool this is adequate, not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that specifies the resource (current conditions), the scope (forecast up to 7 days), the domain (any city), and the data provider (Open-Meteo). Every clause adds value with no surplus.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The combination of the simple two-parameter schema and no output schema means the description is the sole source of behavioral its outcome that it describes a forecast but does not explain the structure of the returned data or units of measurement. An agent can invoke the tool correctly, but the response may be less opaque without more detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must help interpret the two parameters. It does: 'city worldwide' maps loosely to the required city parameter, and 'up to a 7-day forecast' communicates the meaning of the optional days parameter. However, it does not name the days parameter explicitly, nor mention that values are capped at 7, leaving the agent to infer the parameter-to-description mapping.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as providing weather data: 'Current conditions and up to a 7-day forecast.' It is unambiguous and immediately distinguishes itself from the non-weather sibling tools. It stops short of a full 5 because it lacks an explicit action verb ('get', 'retrieve'), relying on the noun phrase to imply the operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description conveys the scope of use: 'any city worldwide,' which clearly signals when an agent should invoke this tool (any current-conditions or multi-day forecast request). No dedicated weather alternative exists among the siblings, so explicit exclusions are unnecessary; context alone provides sufficient guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.